- Machine Learning: other versions:
- What is Elastic Machine Learning?
- Setup and security
- Anomaly detection
- Finding anomalies
- Tutorial: Getting started with anomaly detection
- Advanced concepts
- API quick reference
- How-tos
- Generating alerts for anomaly detection jobs
- Aggregating data for faster performance
- Altering data in your datafeed with runtime fields
- Customizing detectors with custom rules
- Detecting anomalous categories of data
- Reverting to a model snapshot
- Detecting anomalous locations in geographic data
- Mapping anomalies by location
- Adding custom URLs to machine learning results
- Anomaly detection jobs from visualizations
- Exporting and importing machine learning jobs
- Resources
- Data frame analytics
- Natural language processing
IMPORTANT: No additional bug fixes or documentation updates
will be released for this version. For the latest information, see the
current release documentation.
API quick reference
editAPI quick reference
editAll machine learning anomaly detection endpoints have the following base:
/_ml/
The main resources can be accessed with a variety of endpoints:
-
/anomaly_detectors/
: Create and manage anomaly detection jobs -
/calendars/
: Create and manage calendars and scheduled events -
/datafeeds/
: Select data from Elasticsearch to be analyzed -
/filters/
: Create and manage filters for custom rules -
/results/
: Access the results of an anomaly detection job -
/model_snapshots/
: Manage model snapshots
For a full list, see Machine learning anomaly detection APIs.
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